A Novel Machine Learning Framework for Predicting Lung Cancer from Clinical and Imaging Data

Abstract

Lung cancer is one of the most common and lethal cancers globally, and early detection plays a crucial role in improving patient survival. Traditional diagnostic techniques rely on manual analysis of medical images, which can be time-consuming and susceptible to human error. This paper discusses the use of AI tools in the early detection of lung cancer. This can be used to improve the detection accuracy significantly as against conventional methods, thereby allowing AI assistance to radiologists to make more accurate and timely diagnoses. The framework further allows it to be scaled and adapted for different imaging modalities to be implemented in real clinical settings. This research will show the transformative impacts that AI has on healthcare, especially against diseases like lung cancer, where early detection is key.

Introduction

Lung cancer is a leading cause of death globally, largely due to late diagnosis. Traditional medical imaging methods for detecting lung cancer are time-consuming and prone to human error, especially in early stages where abnormalities are small. Recent advances in AI, particularly Transfer Learning and deep learning, have shown promise in automating lung cancer detection from CT images, improving diagnostic speed, accuracy, and reducing errors.

The proposed system workflow involves collecting a curated dataset of lung images (both normal and cancerous), applying preprocessing techniques (resizing, normalization, augmentation), and then segmenting the images using K-means clustering. Important features are extracted via Linear Discriminant Analysis (LDA), followed by classification to distinguish between benign and malignant tumors. This workflow enhances early diagnosis and treatment planning.

To improve segmentation and accuracy, the system incorporates a Neuro-Genetic approach combining Artificial Neural Networks (ANN) and Genetic Algorithms, which helps optimize image thresholding and segmentation. Preprocessing steps such as histogram equalization and noise reduction are applied to improve image quality. Datasets like JSRT and LIDC were used for testing.

Results demonstrate that the system effectively enhances CT image quality through advanced registration and fusion techniques (MRR and DWT-PCAv), allowing better visualization of lung nodules. The classification achieves high precision in detecting and differentiating malignant from benign nodules, promising to aid radiologists in real-time, automated lung cancer diagnosis with improved outcomes.

Conclusion

This paper reviews various steps of lung cancer detection and formation of the lung cancer detection system using Transfer Learning, illustrating the high potential of AI in improving early diagnosis of lung cancer. With high accuracy, sensitivity, and specificity, the system represents a promising tool for supporting healthcare professionals in making informed decisions. While limitations still exist, furthering work to overcome these will lead to an evolution of AI-based diagnostic tools in clinical medicine towards improved management and patient care for lung cancer.

References

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Copyright

Copyright © 2025 Lokesh S Wandhare, Pranay R Chippawar, Pranay V Tijare, Yash S Kosare, Girish B Umaratkar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.